Named Entity Recognition and Relation Extraction are two crucial and
challenging subtasks in the field of Information Extraction. Despite the
successes achieved by the traditional approaches, fundamental research
questions remain open. First, most recent studies use parameter sharing for a
single subtask or shared features for both two subtasks, ignoring their
semantic differences. Second, information interaction mainly focuses on the two
subtasks, leaving the fine-grained informtion interaction among the
subtask-specific features of encoding subjects, relations, and objects
unexplored. Motivated by the aforementioned limitations, we propose a novel
model to jointly extract entities and relations. The main novelties are as
follows: (1) We propose to decouple the feature encoding process into three
parts, namely encoding subjects, encoding objects, and encoding relations.
Thanks to this, we are able to use fine-grained subtask-specific features. (2)
We propose novel inter-aggregation and intra-aggregation strategies to enhance
the information interaction and construct individual fine-grained
subtask-specific features, respectively. The experimental results demonstrate
that our model outperforms several previous state-of-the-art models. Extensive
additional experiments further confirm the effectiveness of our model.

我们提出了一种新模型，旨在联合提取实体和关系，通过解耦特征编码过程并使用细粒度的子任务特定特征以及采用新的信息交互策略来增强模型性能，并在实验中展现了优于现有模型的结果。